class_docstr: TADE Layer module.
docstr: Initilize TADELayer module.
  
  Args:
      in_channels (int): Number of input channles.
      aux_channels (int): Number of auxirialy channles.
      kernel_size (int): Kernel size.
      bias (bool): Whether to use bias parameter in conv.
      upsample_factor (int): Upsample factor.
      upsample_mode (str): Upsample mode.
docstr_tok: ["Initilize","TADELayer","module",".","Args",":","in_channels","(","int",")",":","Number","of","input","channles",".","aux_channels","(","int",")",":","Number","of","auxirialy","channles",".","kernel_size","(","int",")",":","Kernel","size",".","bias","(","bool",")",":","Whether","to","use","bias","parameter","in","conv",".","upsample_factor","(","int",")",":","Upsample","factor",".","upsample_mode","(","str",")",":","Upsample","mode","."]
code: def __init__(
      self,
      in_channels: int = 64,
      aux_channels: int = 80,
      kernel_size: int = 9,
      bias: bool = True,
      upsample_factor: int = 2,
      upsample_mode: str = "nearest",
  ):
      """Initilize TADELayer module.
  
      Args:
          in_channels (int): Number of input channles.
          aux_channels (int): Number of auxirialy channles.
          kernel_size (int): Kernel size.
          bias (bool): Whether to use bias parameter in conv.
          upsample_factor (int): Upsample factor.
          upsample_mode (str): Upsample mode.
  
      """
      super().__init__()
      self.norm = torch.nn.InstanceNorm1d(in_channels)
      self.aux_conv = torch.nn.Sequential(
          torch.nn.Conv1d(
              aux_channels,
              in_channels,
              kernel_size,
              1,
              bias=bias,
              padding=(kernel_size - 1) // 2,
          ),
          # NOTE(kan-bayashi): Use non-linear activation?
      )
      self.gated_conv = torch.nn.Sequential(
          torch.nn.Conv1d(
              in_channels,
              in_channels * 2,
              kernel_size,
              1,
              bias=bias,
              padding=(kernel_size - 1) // 2,
          ),
          # NOTE(kan-bayashi): Use non-linear activation?
      )
      self.upsample = torch.nn.Upsample(
          scale_factor=upsample_factor, mode=upsample_mode
      )
  
file_path: espnet/espnet2/gan_tts/style_melgan/tade_res_block.py
imports: from functools import partial
  import torch
label_desc: Use image node_id 1 to create a new TADELayer object from inherited base classes: torch.nn with example: obj = TADELayer(in_channels, aux_channels, kernel_size, bias, upsample_factor, upsample_mode)